PyLate model based on FremyCompany/BioLORD-2023

This is a PyLate model finetuned from FremyCompany/BioLORD-2023. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

Model Type: PyLate model
Base model: FremyCompany/BioLORD-2023
Document Length: 96 tokens
Query Length: 96 tokens
Output Dimensionality: 128 tokens
Similarity Function: MaxSim

Model Sources

Documentation: PyLate Documentation
Repository: PyLate on GitHub
Hugging Face: PyLate models on Hugging Face

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 95, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search.

Indexing documents

Load the ColBERT model and initialize the PLAID index, then encode and index your documents:

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

# Step 2: Initialize the PLAID index
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Training Details

Training Dataset

Unnamed Dataset

Size: 599,884 training samples
Columns: query, positive, query_id, target_id, group_id, negative_0, negative_1, and negative_2

Approximate statistics based on the first 1000 samples:

	query	positive	query_id	target_id	group_id	negative_0	negative_1	negative_2
type	string	string	string	int	int	string	string	string
details	min: 5 tokens mean: 17.87 tokens max: 96 tokens	min: 4 tokens mean: 18.15 tokens max: 96 tokens	min: 9 tokens mean: 10.75 tokens max: 13 tokens	596894: ~0.10% 701322: ~0.10% 702865: ~0.10% 702867: ~0.10% 703470: ~0.10% 708298: ~0.10% 713825: ~0.10% 724394: ~0.10% 729855: ~0.10% 738156: ~0.10% 739363: ~0.10% 741575: ~0.10% 742041: ~0.10% 743670: ~0.10% 746572: ~0.10% 746895: ~0.10% 747876: ~0.10% 749932: ~0.10% 750142: ~0.10% 753631: ~0.10% 756089: ~0.10% 778765: ~0.10% 779414: ~0.10% 780169: ~0.10% 780312: ~0.10% 780444: ~0.10% 786431: ~0.10% 790254: ~0.10% 792724: ~0.10% 793327: ~0.10% 798875: ~0.10% 838283: ~0.10% 838562: ~0.10% 838652: ~0.10% 840051: ~0.10% 840142: ~0.10% 840521: ~0.10% 840973: ~0.10% 843222: ~0.10% 843914: ~0.10% 844656: ~0.10% 846846: ~0.10% 847967: ~0.10% 848752: ~0.10% 849505: ~0.10% 852270: ~0.10% 852667: ~0.10% 852805: ~0.10% 852939: ~0.10% 853816: ~0.10% 854442: ~0.10% 854766: ~0.10% 854966: ~0.10% 855577: ~0.10% 855971: ~0.10% 856217: ~0.10% 856374: ~0.10% 857739: ~0.10% 859425: ~0.10% 859649: ~0.10% 860315: ~0.10% 898338: ~0.10% 901845: ~0.10% 902766: ~0.10% 902807: ~0.10% 902950: ~0.10% 903711: ~0.10% 904490: ~0.10% 904501: ~0.10% 904542: ~0.10% 910888: ~0.10% 911101: ~0.10% 911354: ~0.10% 911735: ~0.10% 913440: ~0.10% 915175: ~0.10% 915518: ~0.10% 916773: ~0.10% 916943: ~0.10% 920301: ~0.10% 920458: ~0.10% 920949: ~0.10% 935529: ~0.10% 938205: ~0.10% 942028: ~0.10% 943885: ~0.10% 950056: ~0.10% 951511: ~0.10% 952540: ~0.10% 955375: ~0.20% 958999: ~0.10% 961168: ~0.10% 964854: ~0.10% 965132: ~0.10% 966913: ~0.10% 967436: ~0.10% 967563: ~0.10% 968538: ~0.10% 974886: ~0.10% 975589: ~0.10% 992716: ~0.10% 1000995: ~0.10% 1001419: ~0.10% 1111242: ~0.10% 1112807: ~0.10% 1113346: ~0.10% 1114220: ~0.10% 1115008: ~0.10% 1115588: ~0.10% 1124957: ~0.10% 1125315: ~0.10% 1126872: ~0.10% 1126952: ~0.10% 1133229: ~0.10% 1134440: ~0.10% 1134770: ~0.10% 1140088: ~0.10% 1153013: ~0.10% 1154062: ~0.10% 1156378: ~0.10% 1177480: ~0.10% 1201620: 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~0.10% 19049081: ~0.10% 19050216: ~0.10% 19052896: ~0.10% 19055344: ~0.10% 19057397: ~0.10% 19057715: ~0.10% 19058399: ~0.10% 19061821: ~0.10% 19064496: ~0.10% 19065540: ~0.10% 19065814: ~0.10% 19069090: ~0.10% 19072058: ~0.20% 19072321: ~0.10% 19072387: ~0.10% 19073094: ~0.10% 19073187: ~0.10% 19073276: ~0.10% 19073642: ~0.10% 19074672: ~0.10% 19074843: ~0.10% 19074932: ~0.10% 19074975: ~0.10% 19075010: ~0.10% 19075346: ~0.10% 19076442: ~0.10% 19076595: ~0.10% 19077462: ~0.10% 19077610: ~0.10% 19077638: ~0.10% 19077738: ~0.20% 19077740: ~0.10% 19078924: ~0.10% 19078938: ~0.10% 19078960: ~0.10% 19079113: ~0.10% 19079779: ~0.10% 19079835: ~0.10% 19081294: ~0.10% 19082112: ~0.10% 19082452: ~0.10% 19082891: ~0.10% 19084696: ~0.10% 19086939: ~0.10% 19087102: ~0.10% 19089269: ~0.10% 19090470: ~0.10% 19091486: ~0.10% 19092849: ~0.10% 19095288: ~0.10% 19095721: ~0.10% 19096714: ~0.10% 19099840: ~0.10% 19100417: ~0.10% 19101604: ~0.10% 19101675: ~0.10% 19102101: ~0.10% 19102151: ~0.10% 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~0.10% 19115638: ~0.10% 19116545: ~0.10% 19116836: ~0.10% 19117417: ~0.10% 19117912: ~0.10% 19120731: ~0.10% 19121298: ~0.10% 19122306: ~0.10% 19123240: ~0.10% 19123348: ~0.10% 19123592: ~0.10% 19125201: ~0.10% 19126145: ~0.10% 19126587: ~0.10% 19127009: ~0.10% 19129738: ~0.10% 19134124: ~0.10% 19135816: ~0.10% 21022365: ~0.10% 21023024: ~0.10% 21025502: ~0.10% 21026454: ~0.10% 21026676: ~0.10% 21028973: ~0.10% 21031144: ~0.10% 21032887: ~0.10% 21033401: ~0.10% 21041793: ~0.10% 21048875: ~0.10% 21057847: ~0.10% 21060247: ~0.10% 21065441: ~0.10% 21066982: ~0.10% 21072610: ~0.10% 21073201: ~0.10% 21074329: ~0.10% 21074893: ~0.10% 21076317: ~0.10% 21076772: ~0.10% 21077411: ~0.10% 21079968: ~0.10% 21081645: ~0.10% 21085020: ~0.10% 21086280: ~0.10% 21090283: ~0.10% 21090512: ~0.10% 21090953: ~0.10% 21094012: ~0.10% 21096061: ~0.10% 21096188: ~0.10% 21096988: ~0.10% 21099992: ~0.10% 21104348: ~0.10% 21113687: ~0.10% 21115297: ~0.10% 21118779: ~0.10% 21120538: ~0.10% 21120876: ~0.10% 21121011: ~0.10% 21122992: ~0.10% 21123459: ~0.10% 21123760: ~0.10% 21124351: ~0.10% 21125241: ~0.10% 21126678: ~0.10% 21130529: ~0.10% 21131311: ~0.10% 21132604: ~0.10% 21136121: ~0.10% 21140670: ~0.10% 21144480: ~0.10% 21148844: ~0.10% 21149414: ~0.10% 21150843: ~0.10% 21151724: ~0.10% 21153031: ~0.10% 21154990: ~0.10% 21159867: ~0.10% 21162527: ~0.10% 21162656: ~0.10% 21163482: ~0.10% 21163513: ~0.10% 21169105: ~0.10% 21170863: ~0.10% 21173552: ~0.10% 21173981: ~0.10% 35129441: ~0.10% 35146162: ~0.10% 35412933: ~0.10% 35603079: ~0.10% 35603174: ~0.10% 35603581: ~0.10% 35603595: ~0.10% 35604022: ~0.10% 35604667: ~0.10% 35604742: ~0.10% 35605629: ~0.10% 35606029: ~0.10% 35606306: ~0.10% 35776312: ~0.10% 35781754: ~0.10% 35782577: ~0.10% 35795663: ~0.10% 35848427: ~0.10% 35850582: ~0.10% 35852062: ~0.10% 35852288: ~0.10% 35852328: ~0.10% 35852567: ~0.10% 35852697: ~0.10% 35853106: ~0.10% 35853660: ~0.10% 35853891: ~0.10% 35854015: ~0.10% 35854136: ~0.10% 35854564: ~0.10% 35854578: ~0.10% 35854999: ~0.10% 35855526: ~0.10% 35855654: ~0.10% 35856519: ~0.10% 35856709: ~0.10% 35857230: ~0.10% 35858107: ~0.10% 35859782: ~0.10% 35859890: ~0.10% 35860828: ~0.10% 35860889: ~0.10% 35861094: ~0.10% 35861116: ~0.10% 35861123: ~0.10% 35862253: ~0.10% 35863227: ~0.10% 35863412: ~0.10% 35864671: ~0.10% 35865056: ~0.10% 35865797: ~0.10% 35867314: ~0.10% 35868522: ~0.10% 35868631: ~0.10% 35868976: ~0.10% 35869488: ~0.10% 35869729: ~0.10% 35870282: ~0.10% 35870314: ~0.10% 35870824: ~0.10% 35871224: ~0.10% 35872072: ~0.20% 35872931: ~0.10% 35873332: ~0.10% 35874810: ~0.10% 35886324: ~0.10% 35886499: ~0.10% 36053028: ~0.10% 36053818: ~0.10% 36057797: ~0.10% 36062365: ~0.10% 36068019: ~0.10% 36068607: ~0.10% 36074320: ~0.10% 36074619: ~0.10% 36075320: ~0.10% 36120203: ~0.10% 36120528: ~0.10% 36120639: ~0.10% 36249209: ~0.10% 36249580: ~0.10% 36406426: ~0.10% 36411562: ~0.10% 36857476: ~0.10% 36859258: ~0.10% 36861569: ~0.10% 36878779: ~0.10% 36879879: ~0.10% 36881596: ~0.10% 36883380: ~0.20% 36883672: ~0.10% 36886615: ~0.10% 36887223: ~0.10% 36889762: ~0.10% 36891127: ~0.10% 36893482: ~0.10% 36897407: ~0.10% 36928121: ~0.10% 36931775: ~0.10% 36932085: ~0.10% 36941493: ~0.10% 36944525: ~0.10% 36945389: ~0.10% 36947151: ~0.10% 36956677: ~0.10% 36964651: ~0.10% 36965105: ~0.10% 37002369: ~0.10% 37497324: ~0.10% 37497460: ~0.10% 37498491: ~0.10% 37498652: ~0.10% 37499581: ~0.10% 40000224: ~0.10% 40000233: ~0.10% 40000499: ~0.10% 40001160: ~0.10% 40002706: ~0.10% 40005733: ~0.10% 40008941: ~0.10% 40009472: ~0.10% 40009842: ~0.10% 40011519: ~0.10% 40013138: ~0.10% 40014736: ~0.10% 40017062: ~0.10% 40018379: ~0.10% 40019490: ~0.10% 40023487: ~0.10% 40023500: ~0.10% 40024789: ~0.10% 40025590: ~0.10% 40025728: ~0.10% 40026127: ~0.10% 40027982: ~0.10% 40029837: ~0.10% 40030425: ~0.10% 40032109: ~0.10% 40037560: ~0.10% 40038376: ~0.10% 40039439: ~0.30% 40039909: ~0.10% 40040334: ~0.10% 40040683: ~0.10% 40040685: ~0.10% 40040917: ~0.10% 40041668: ~0.10% 40042277: ~0.10% 40042619: ~0.10% 40043312: ~0.10% 40044251: ~0.10% 40044538: ~0.10% 40044587: ~0.10% 40045119: ~0.10% 40046823: ~0.10% 40052516: ~0.10% 40052547: ~0.10% 40054158: ~0.10% 40054628: ~0.10% 40055251: ~0.10% 40055814: ~0.10% 40056207: ~0.10% 40056894: ~0.10% 40057469: ~0.10% 40057652: ~0.10% 40058892: ~0.10% 40058964: ~0.10% 40059398: ~0.10% 40061537: ~0.10% 40064205: ~0.10% 40064847: ~0.10% 40065792: ~0.10% 40066077: ~0.10% 40066417: ~0.10% 40069718: ~0.10% 40069977: ~0.10% 40071520: ~0.10% 40077340: ~0.10% 40078001: ~0.10% 40078301: ~0.10% 40080294: ~0.10% 40083797: ~0.10% 40084190: ~0.10% 40089490: ~0.10% 40089954: ~0.10% 40090204: ~0.10% 40091035: ~0.10% 40091096: ~0.10% 40091338: ~0.10% 40092942: ~0.10% 40093804: ~0.10% 40095247: ~0.20% 40096421: ~0.10% 40098064: ~0.10% 40099053: ~0.10% 40099332: ~0.10% 40099452: ~0.20% 40100681: ~0.10% 40100732: ~0.10% 40101049: ~0.10% 40101922: ~0.10% 40101973: ~0.10% 40102099: ~0.10% 40102258: ~0.10% 40102479: ~0.10% 40127090: ~0.10% 40127234: ~0.10% 40130811: ~0.10% 40132149: ~0.10% 40140902: ~0.10% 40145354: ~0.10% 40149242: ~0.10% 40149524: ~0.10% 40153863: ~0.10% 40156496: ~0.10% 40161610: ~0.10% 40162577: ~0.10% 40162665: ~0.10% 40163478: ~0.10% 40163638: ~0.10% 40164884: ~0.10% 40165164: ~0.10% 40165321: ~0.10% 40165476: ~0.10% 40165711: ~0.20% 40165727: ~0.10% 40165762: ~0.10% 40165789: ~0.20% 40166828: ~0.10% 40167040: ~0.10% 40167443: ~0.10% 40168239: ~0.10% 40168549: ~0.10% 40171646: ~0.10% 40171849: ~0.10% 40172143: ~0.10% 40174061: ~0.10% 40174493: ~0.10% 40174852: ~0.10% 40175394: ~0.10% 40175754: ~0.10% 40177823: ~0.10% 40180873: ~0.10% 40184083: ~0.10% 40184719: ~0.10% 40213232: ~0.10% 40213311: ~0.10% 40220405: ~0.10% 40221901: ~0.10% 40222833: ~0.10% 40222834: ~0.10% 40223107: ~0.10% 40223108: ~0.10% 40223111: ~0.10% 40223765: ~0.20% 40223778: ~0.10% 40223828: ~0.10% 40223885: ~0.10% 40223901: ~0.10% 40224153: ~0.10% 40224172: ~0.10% 40226742: ~0.10% 40228548: ~0.10% 40229547: ~0.10% 40231370: ~0.10% 40232707: ~0.10% 40232726: ~0.10% 40232742: ~0.10% 40233282: ~0.10% 40234339: ~0.10% 40234436: ~0.10% 40234524: ~0.10% 40234539: ~0.10% 40236424: ~0.10% 40236647: ~0.10% 40238016: ~0.10% 40238415: ~0.10% 40239986: ~0.10% 40240800: ~0.10% 40241281: ~0.10% 40242524: ~0.10% 40244029: ~0.10% 40244464: ~0.10% 40709911: ~0.10% 40710274: ~0.10% 40714988: ~0.10% 40721819: ~0.10% 40730313: ~0.10% 40731044: ~0.10% 40736399: ~0.10% 40738756: ~0.10% 40739273: ~0.10% 40740639: ~0.10% 40744760: ~0.10% 40745502: ~0.10% 40749647: ~0.10% 40750090: ~0.10% 40798619: ~0.10% 40799180: ~0.10% 40836307: ~0.10% 41021185: ~0.10% 41062462: ~0.10% 41111655: ~0.10% 41111900: ~0.10% 41169880: ~0.10% 41178395: ~0.10% 41327484: ~0.10% 42479093: ~0.10% 42482061: ~0.10% 42482203: ~0.10% 42482595: ~0.10% 42705327: ~0.10% 42709322: ~0.10% 42800602: ~0.10% 42801309: ~0.10% 42899155: ~0.10% 42899638: ~0.10% 42900014: ~0.10% 42900548: ~0.10% 42901086: ~0.10% 42902282: ~0.10% 42902550: ~0.10% 42902570: ~0.10% 42903119: ~0.10% 42903228: ~0.10% 42903409: ~0.10% 42904111: ~0.10% 42904177: ~0.10% 42925100: ~0.10% 42930463: ~0.10% 42935618: ~0.10% 42938495: ~0.10% 42942477: ~0.10% 42959730: ~0.10% 42960276: ~0.10% 42960447: ~0.10% 42960605: ~0.10% 42960868: ~0.10% 42961124: ~0.10% 42961948: ~0.10% 42962169: ~0.10% 42969131: ~0.10% 42971634: ~0.10% 43011476: ~0.10% 43011938: ~0.10% 43012210: ~0.10% 43012233: ~0.10% 43013770: ~0.10% 43013895: ~0.10% 43024692: ~0.10% 43171203: ~0.10% 43256658: ~0.10% 43260634: ~0.10% 43264884: ~0.10% 43266591: ~0.10% 43268649: ~0.10% 43268781: ~0.10% 43269154: ~0.10% 43270222: ~0.10% 43275122: ~0.10% 43275590: ~0.10% 43276811: ~0.10% 43277265: ~0.10% 43277791: ~0.10% 43278923: ~0.10% 43279463: ~0.10% 43279939: ~0.10% 43281931: ~0.10% 43281949: ~0.10% 43282683: ~0.10% 43283817: ~0.10% 43284039: ~0.10% 43284498: ~0.10% 43285243: ~0.10% 43286026: ~0.10% 43288139: ~0.10% 43288231: ~0.10% 43288663: ~0.10% 43292044: ~0.10% 43294682: ~0.10% 43514798: ~0.10% 43516486: ~0.10% 43525903: ~0.10% 43532926: ~0.10% 43610123: ~0.10% 43705368: ~0.10% 43790653: ~0.10% 44032453: ~0.10% 44130738: ~0.10% 44814322: ~0.10% 44817922: ~0.10% 45775569: ~0.10% 45775571: ~0.10% 45775773: ~0.10% 45892566: ~0.10% 46221230: ~0.10% 46221433: ~0.10% 46233735: ~0.10% 46233988: ~0.10% 46234031: ~0.10% 46275260: ~0.10% 46275602: ~0.10% 46275620: ~0.10% 46275677: ~0.10% 46276153: ~0.10% 46287383: ~0.20% 46287412: ~0.10% 46287541: ~0.10%	min: 5 tokens mean: 19.58 tokens max: 96 tokens	min: 5 tokens mean: 20.65 tokens max: 96 tokens	min: 5 tokens mean: 21.6 tokens max: 96 tokens

Samples:

query	positive	query_id	target_id	group_id	negative_0	negative_1	negative_2
`donepezil (gh) 10 mg tablet, 60`	`donepezil 10 mg oral tablet box of 60`	`q2955867_0`	`43293533`	`715997`	`donepezil 10 mg oral tablet box of 28`	`donepezil 10 mg delayed release oral tablet box of 56`	`donepezil 10 mg delayed release oral tablet box of 120`
`头孢替唑钠 2.0克注射剂 [益替欣]`	`ceftezole sodium 2000 mg injectable solution [yitixin]`	`q35876872_1`	`35871757`	`35872072`	`ceftezole sodium 4000 mg injectable solution [yitixin]`	`ceftezole sodium 4000 mg injectable solution [yitixin]`	`ceftezole sodium 4000 mg injectable solution [yitixin]`
`acetaminophen 325mg, tramadol hcl 37.5mg`	`acetaminophen 325 mg / tramadol 37.5 mg oral tablet [tramacet]`	`q42386298_1`	`21111882`	`21131311`	`azathioprine 50 mg oral tablet [azanin]`	`olanzapine 20 mg oral tablet [olanzapin 1a pharma]`	`levomenthol 6.5 mg/ml medicated liquid soap [pinimenthol]`

Loss: thirawat_trainer.training.colbert.KDLossWrapper

Evaluation Dataset

Unnamed Dataset

Size: 2,287 evaluation samples
Columns: query, positive, query_id, target_id, group_id, negative_0, negative_1, and negative_2

Approximate statistics based on the first 1000 samples:

	query	positive	query_id	target_id	group_id	negative_0	negative_1	negative_2
type	string	string	string	int	int	string	string	string
details	min: 5 tokens mean: 16.89 tokens max: 67 tokens	min: 5 tokens mean: 16.87 tokens max: 96 tokens	min: 9 tokens mean: 10.77 tokens max: 13 tokens	529411: ~0.10% 705182: ~0.10% 715458: ~1.10% 725172: ~0.10% 742183: ~0.10% 746843: ~0.10% 751282: ~0.50% 766844: ~0.20% 778353: ~0.30% 779185: ~0.10% 789652: ~0.10% 793898: ~0.20% 842692: ~0.10% 844619: ~0.10% 846549: ~0.10% 847191: ~0.10% 847968: ~0.10% 849238: ~0.10% 851288: ~0.30% 852961: ~0.10% 853204: ~0.10% 856802: ~0.10% 857859: ~0.10% 859815: ~0.10% 861178: ~0.10% 861356: ~0.10% 898319: ~0.10% 903753: ~0.70% 903904: ~0.20% 916178: ~0.20% 917339: ~0.70% 922762: ~0.20% 962398: ~0.10% 963355: ~1.00% 964326: ~0.10% 966454: ~0.10% 968639: ~0.30% 993770: ~0.20% 1100303: ~0.10% 1100333: ~0.10% 1103640: ~0.10% 1113672: ~1.80% 1115125: ~0.40% 1145582: ~0.10% 1146810: ~0.10% 1150347: ~3.00% 1150870: ~0.30% 1150925: ~0.20% 1153428: ~0.20% 1163945: ~0.90% 1181809: ~0.10% 1308877: ~0.70% 1350332: ~0.60% 1387578: ~0.10% 1396092: ~0.10% 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19136626: ~0.20% 19136638: ~0.10% 19137312: ~0.40% 21022535: ~0.10% 21025293: ~0.10% 21025516: ~0.10% 21031174: ~0.10% 21042022: ~0.10% 21050406: ~0.10% 21054437: ~0.10% 21056256: ~0.10% 21061791: ~0.10% 21065356: ~0.10% 21073187: ~0.10% 21075193: ~0.10% 21076185: ~0.10% 21086101: ~0.10% 21100665: ~0.10% 21109793: ~0.20% 21111652: ~0.10% 21112541: ~0.10% 21119204: ~0.10% 21119416: ~0.20% 21121684: ~0.10% 21147764: ~0.30% 21150873: ~0.10% 21152466: ~0.10% 21154890: ~0.10% 21155798: ~0.10% 21172345: ~0.10% 35157015: ~0.10% 35200094: ~0.10% 35201112: ~0.10% 35602993: ~0.10% 35603139: ~0.10% 35603360: ~0.10% 35603717: ~0.10% 35605255: ~0.20% 35605598: ~0.30% 35606381: ~0.10% 35778226: ~0.10% 35849615: ~0.10% 35849752: ~0.20% 35849843: ~0.10% 35850230: ~0.10% 35850792: ~0.10% 35851095: ~0.10% 35851569: ~0.20% 35852008: ~0.10% 35852035: ~0.20% 35853050: ~0.10% 35853308: ~0.10% 35853475: ~0.10% 35853951: ~0.10% 35854063: ~0.10% 35855053: ~0.20% 35855295: ~0.10% 35856002: ~0.20% 35856403: ~0.10% 35857140: ~0.10% 35857465: ~0.10% 35858409: ~0.10% 35859611: ~0.20% 35859701: ~0.10% 35859956: ~0.20% 35860653: ~0.30% 35861327: ~0.10% 35861499: ~0.20% 35863370: ~0.20% 35863520: ~0.10% 35863895: ~0.10% 35864704: ~0.10% 35864978: ~0.20% 35865164: ~0.20% 35865556: ~0.10% 35866751: ~0.10% 35867051: ~0.20% 35867650: ~0.10% 35869475: ~0.10% 35869521: ~0.10% 35869526: ~0.20% 35870345: ~0.10% 35870491: ~0.10% 35870800: ~0.10% 35872084: ~0.20% 35872982: ~0.20% 35873158: ~0.10% 36052362: ~0.10% 36059702: ~0.10% 36065031: ~0.10% 36120676: ~0.10% 36269476: ~0.10% 36408005: ~0.10% 36892095: ~0.10% 36930572: ~0.10% 36942308: ~0.10% 37497243: ~0.20% 37497991: ~0.10% 37499597: ~0.10% 40000191: ~0.10% 40000823: ~0.10% 40000895: ~0.10% 40006525: ~0.10% 40008978: ~0.10% 40009853: ~0.10% 40009896: ~0.10% 40012752: ~0.30% 40014094: ~0.20% 40015130: ~0.30% 40015379: ~0.10% 40018632: ~0.10% 40020313: ~0.10% 40021303: ~0.20% 40022045: ~0.10% 40023567: ~0.20% 40024030: ~0.10% 40026184: ~0.10% 40026724: ~0.10% 40027988: ~0.30% 40029800: ~0.40% 40029802: ~0.10% 40031380: ~0.10% 40036705: ~0.10% 40037331: ~0.10% 40041668: ~0.10% 40044993: ~0.10% 40045753: ~0.10% 40045755: ~0.10% 40047199: ~1.00% 40048641: ~0.20% 40049782: ~0.10% 40054144: ~0.10% 40054158: ~0.10% 40056528: ~0.10% 40057424: ~0.10% 40059833: ~0.10% 40062549: ~0.30% 40065515: ~0.10% 40068160: ~0.20% 40070098: ~0.10% 40072151: ~0.40% 40075734: ~0.20% 40076362: ~0.10% 40077664: ~0.10% 40082136: ~0.20% 40083021: ~0.10% 40083592: ~0.10% 40086368: ~0.10% 40086374: ~0.10% 40088069: ~0.10% 40088510: ~0.10% 40091031: ~0.20% 40091096: ~0.70% 40092781: ~0.10% 40092942: ~0.10% 40093769: ~0.10% 40095502: ~0.60% 40096141: ~0.10% 40096233: ~0.10% 40097452: ~0.10% 40098114: ~0.10% 40099132: ~0.10% 40099133: ~0.10% 40100588: ~0.20% 40101798: ~0.20% 40102009: ~0.70% 40102099: ~0.10% 40102493: ~0.10% 40102767: ~0.10% 40109001: ~1.70% 40129549: ~0.10% 40132149: ~0.40% 40139114: ~0.40% 40140180: ~0.10% 40143939: ~0.10% 40159890: ~0.10% 40161252: ~0.10% 40161497: ~0.10% 40162087: ~0.10% 40163523: ~1.30% 40163540: ~0.10% 40164091: ~0.10% 40166035: ~0.50% 40166571: ~0.10% 40166901: ~0.50% 40168812: ~0.10% 40170883: ~0.10% 40174364: ~0.10% 40185276: ~0.10% 40213296: ~0.10% 40221135: ~0.10% 40221362: ~0.10% 40221377: ~0.20% 40222018: ~0.10% 40222376: ~0.10% 40222946: ~0.20% 40223074: ~0.10% 40223078: ~0.10% 40224436: ~0.70% 40226634: ~0.10% 40227524: ~0.30% 40227933: ~0.10% 40229499: ~0.10% 40229691: ~0.20% 40232185: ~0.10% 40238630: ~0.30% 40240215: ~0.30% 40240642: ~0.10% 40241335: ~0.10% 40242283: ~0.10% 40242661: ~0.30% 40243616: ~0.10% 40243866: ~0.60% 40244241: ~0.10% 40726652: ~0.10% 40729516: ~0.10% 40743726: ~0.10% 40750268: ~0.10% 40753245: ~0.10% 40798773: ~0.20% 40892708: ~0.60% 40957214: ~0.10% 40964169: ~0.10% 41013472: ~0.10% 41014233: ~0.10% 41053478: ~0.10% 41086226: ~0.10% 41127461: ~0.10% 41337509: ~0.10% 41368941: ~0.10% 42479758: ~0.10% 42482607: ~0.10% 42629345: ~0.20% 42706502: ~0.10% 42708018: ~0.10% 42873639: ~0.20% 42874212: ~0.10% 42874253: ~0.10% 42900402: ~0.10% 42901417: ~0.10% 42901939: ~0.20% 42902184: ~0.10% 42902560: ~0.10% 42903074: ~0.10% 42919824: ~0.10% 42920193: ~0.10% 42924604: ~0.10% 42926104: ~0.10% 42929184: ~0.10% 42935133: ~0.20% 42938060: ~0.10% 42938798: ~0.10% 42940012: ~0.10% 42941920: ~0.10% 42943981: ~0.10% 42948684: ~0.10% 42948763: ~0.10% 42952357: ~0.10% 42956690: ~0.10% 42958499: ~0.10% 42959173: ~0.10% 42961502: ~0.10% 42962100: ~0.10% 42963730: ~0.10% 42963918: ~0.10% 42964614: ~0.10% 42966177: ~0.10% 42969711: ~0.10% 42971191: ~0.10% 42973884: ~0.10% 43009035: ~0.10% 43009095: ~0.10% 43012195: ~0.10% 43013201: ~0.10% 43255799: ~0.10% 43255876: ~0.10% 43258783: ~0.20% 43261498: ~0.10% 43262076: ~0.20% 43262730: ~0.10% 43267119: ~0.10% 43274735: ~0.10% 43277134: ~0.10% 43278404: ~0.10% 43283762: ~0.10% 43288204: ~0.10% 43294360: ~0.10% 43526425: ~0.10% 43531905: ~0.10% 43532236: ~0.20% 43640125: ~0.10% 43653544: ~0.10% 43781650: ~0.20% 44012547: ~0.30% 44069334: ~0.30% 44071738: ~0.10% 44090960: ~0.10% 44091314: ~0.10% 44107418: ~0.10% 44814411: ~0.10% 45775571: ~0.10% 45775584: ~0.10% 46221239: ~0.10% 46221721: ~0.10% 46234260: ~0.20% 46234270: ~0.20% 46234308: ~0.10% 46234522: ~0.30%	min: 5 tokens mean: 19.25 tokens max: 96 tokens	min: 6 tokens mean: 20.54 tokens max: 96 tokens	min: 7 tokens mean: 21.64 tokens max: 96 tokens

Samples:

query	positive	query_id	target_id	group_id	negative_0	negative_1	negative_2
`clobazam 10mg/5ml oral suspension`	`clobazam 2 mg/ml oral suspension`	`q21276207_0`	`21025670`	`19050832`	`clobazam 0.15 mg/ml oral suspension`	`clobazam 0.5 mg/ml oral suspension`	`clobazam 10 mg/ml oral solution`
`nappy rash soothing and healing (david hosking's) cream, 120 g`	`120000 mg benzoin resin 0.01 mg/mg / camphor 0.01 mg/mg / lanolin 0.5 mg/mg / storax prepared 0.008 mg/mg / zinc oxide 0.15 mg/mg topical ointment [nappy rash soothing and healing]`	`q43329690_0`	`43281402`	`40161252`	`118 ml dibasic sodium phosphate heptahydrate 59.3 mg/ml / sodium phosphate, monobasic 161 mg/ml rectal solution [fleet ready-to-use]`	`1 ml domperidone 10 mg/ml powder for oral suspension [nauzelin]`	`70000 mg arnica extract 0.1 mg/mg topical ointment [arnikae]`
`0.3 g clindamycin phosphate [kexinlin]`	`clindamycin 300 mg [kexinlin]`	`q36644900_0`	`35874078`	`19085270`	`clindamycin 300 mg [zhuoli]`	`clindamycin 300 mg [clindamycin aristo]`	`clindamycin 300 mg [clindamycin billix]`

Loss: thirawat_trainer.training.colbert.KDLossWrapper

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 128
gradient_accumulation_steps: 2
learning_rate: 2e-05
warmup_ratio: 0.1
bf16: True
dataloader_num_workers: 4
remove_unused_columns: False
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 4
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: False
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0021	5	260.9562	-
0.0043	10	243.0724	-
0.0064	15	226.9033	-
0.0085	20	247.598	-
0.0107	25	237.7835	-
0.0128	30	218.3724	-
0.0149	35	208.53	-
0.0171	40	224.4951	-
0.0192	45	207.6082	-
0.0213	50	215.5779	-
0.0235	55	235.3491	-
0.0256	60	221.6164	-
0.0277	65	212.6889	-
0.0299	70	216.2898	-
0.0320	75	231.7364	-
0.0341	80	206.7381	-
0.0363	85	194.5393	-
0.0384	90	212.0394	-
0.0405	95	199.709	-
0.0427	100	208.5693	-
0.0448	105	185.4214	-
0.0469	110	207.6509	-
0.0491	115	207.7226	-
0.0512	120	176.0556	-
0.0533	125	194.877	-
0.0555	130	194.9979	-
0.0576	135	201.5107	-
0.0597	140	191.0996	-
0.0619	145	156.6461	-
0.0640	150	163.2723	-
0.0661	155	160.0	-
0.0683	160	142.0651	-
0.0704	165	147.7512	-
0.0725	170	140.8079	-
0.0747	175	125.8033	-
0.0768	180	139.7856	-
0.0789	185	116.0057	-
0.0811	190	110.8626	-
0.0832	195	109.0995	-
0.0853	200	119.4655	-
0.0875	205	110.2229	-
0.0896	210	112.9283	-
0.0917	215	112.8705	-
0.0939	220	99.1093	-
0.0960	225	84.201	-
0.0981	230	85.2275	-
0.1003	235	73.787	-
0.1024	240	79.8435	-
0.1045	245	67.2795	-
0.1067	250	56.0195	-
0.1088	255	49.6022	-
0.1109	260	52.5486	-
0.1131	265	42.2398	-
0.1152	270	40.5546	-
0.1173	275	40.2339	-
0.1195	280	33.0703	-
0.1216	285	28.2356	-
0.1237	290	25.7024	-
0.1259	295	21.9268	-
0.1280	300	18.9837	-
0.1301	305	14.5069	-
0.1323	310	13.9749	-
0.1344	315	10.4993	-
0.1365	320	10.8756	-
0.1387	325	8.448	-
0.1408	330	7.3985	-
0.1429	335	6.7277	-
0.1451	340	5.369	-
0.1472	345	5.1479	-
0.1493	350	3.9802	-
0.1515	355	3.3223	-
0.1536	360	3.148	-
0.1557	365	3.1232	-
0.1579	370	2.6608	-
0.1600	375	2.6175	-
0.1622	380	2.1366	-
0.1643	385	2.2121	-
0.1664	390	2.2454	-
0.1686	395	2.1155	-
0.1707	400	1.988	-
0.1728	405	1.965	-
0.1750	410	1.8777	-
0.1771	415	1.9024	-
0.1792	420	1.7564	-
0.1814	425	1.7513	-
0.1835	430	1.8298	-
0.1856	435	1.5716	-
0.1878	440	1.5193	-
0.1899	445	1.4442	-
0.1920	450	1.5014	-
0.1942	455	1.4301	-
0.1963	460	1.5181	-
0.1984	465	1.328	-
0.2006	470	1.2831	-
0.2027	475	1.4169	-
0.2048	480	1.6943	-
0.2070	485	1.2827	-
0.2091	490	1.3442	-
0.2112	495	1.3282	-
0.2134	500	1.2025	-
0.2155	505	1.4431	-
0.2176	510	1.2434	-
0.2198	515	1.1499	-
0.2219	520	1.0773	-
0.2240	525	1.2212	-
0.2262	530	1.2723	-
0.2283	535	1.2088	-
0.2304	540	1.1508	-
0.2326	545	1.1426	-
0.2347	550	1.1775	-
0.2368	555	1.2678	-
0.2390	560	1.1158	-
0.2411	565	1.0517	-
0.2432	570	1.1448	-
0.2454	575	1.16	-
0.2475	580	1.0683	-
0.2496	585	1.0468	-
0.2518	590	0.9907	-
0.2539	595	1.1522	-
0.2560	600	1.0523	-
0.2582	605	1.0185	-
0.2603	610	1.0248	-
0.2624	615	0.9046	-
0.2646	620	0.9747	-
0.2667	625	0.9869	-
0.2688	630	1.0425	-
0.2710	635	0.9651	-
0.2731	640	1.0077	-
0.2752	645	0.8222	-
0.2774	650	0.8685	-
0.2795	655	0.8913	-
0.2816	660	0.935	-
0.2838	665	0.967	-
0.2859	670	0.9005	-
0.2880	675	0.8015	-
0.2902	680	0.912	-
0.2923	685	0.9849	-
0.2944	690	1.0628	-
0.2966	695	0.8542	-
0.2987	700	0.911	-
0.3008	705	0.9248	-
0.3030	710	0.8878	-
0.3051	715	0.9006	-
0.3072	720	0.9037	-
0.3094	725	0.8492	-
0.3115	730	0.8886	-
0.3136	735	0.8558	-
0.3158	740	0.8353	-
0.3179	745	0.8278	-
0.3200	750	0.8283	-
0.3222	755	0.867	-
0.3243	760	0.7298	-
0.3264	765	0.874	-
0.3286	770	0.7672	-
0.3307	775	0.8225	-
0.3328	780	0.8248	-
0.3350	785	0.8961	-
0.3371	790	0.8242	-
0.3392	795	0.764	-
0.3414	800	0.7479	-
0.3435	805	0.9007	-
0.3456	810	0.8867	-
0.3478	815	0.8493	-
0.3499	820	0.7158	-
0.3520	825	0.7642	-
0.3542	830	0.7993	-
0.3563	835	0.7103	-
0.3584	840	0.7772	-
0.3606	845	0.8199	-
0.3627	850	0.9472	-
0.3648	855	0.7318	-
0.3670	860	0.6891	-
0.3691	865	0.8185	-
0.3712	870	0.7771	-
0.3734	875	0.7638	-
0.3755	880	1.1106	-
0.3776	885	0.8603	-
0.3798	890	0.9143	-
0.3819	895	0.7764	-
0.3840	900	0.8121	-
0.3862	905	0.7664	-
0.3883	910	0.7424	-
0.3904	915	0.7377	-
0.3926	920	0.8329	-
0.3947	925	0.6529	-
0.3968	930	0.653	-
0.3990	935	0.6713	-
0.4011	940	0.8805	-
0.4032	945	0.7712	-
0.4054	950	0.7399	-
0.4075	955	0.7024	-
0.4096	960	0.7185	-
0.4118	965	0.6943	-
0.4139	970	0.7553	-
0.4160	975	0.5834	-
0.4182	980	0.6494	-
0.4203	985	0.7961	-
0.4224	990	0.6523	-
0.4246	995	0.9619	-
0.4267	1000	0.7668	-
0.4288	1005	0.6901	-
0.4310	1010	0.6974	-
0.4331	1015	0.6458	-
0.4352	1020	0.664	-
0.4374	1025	0.639	-
0.4395	1030	0.7748	-
0.4416	1035	0.666	-
0.4438	1040	0.7466	-
0.4459	1045	0.6738	-
0.4480	1050	0.769	-
0.4502	1055	0.6594	-
0.4523	1060	0.7834	-
0.4544	1065	0.5834	-
0.4566	1070	0.5964	-
0.4587	1075	0.673	-
0.4608	1080	0.6506	-
0.4630	1085	0.6009	-
0.4651	1090	0.628	-
0.4672	1095	0.7201	-
0.4694	1100	0.6409	-
0.4715	1105	0.5792	-
0.4737	1110	0.5869	-
0.4758	1115	0.6068	-
0.4779	1120	0.6184	-
0.4801	1125	0.62	-
0.4822	1130	0.6352	-
0.4843	1135	0.6614	-
0.4865	1140	0.6086	-
0.4886	1145	0.6339	-
0.4907	1150	0.6728	-
0.4929	1155	0.6508	-
0.4950	1160	0.6442	-
0.4971	1165	0.7078	-
0.4993	1170	0.7788	-
0.5014	1175	0.7097	-
0.5035	1180	0.5971	-
0.5057	1185	0.6299	-
0.5078	1190	0.6348	-
0.5099	1195	0.6403	-
0.5121	1200	0.655	-
0.5142	1205	0.617	-
0.5163	1210	0.586	-
0.5185	1215	0.6441	-
0.5206	1220	0.5532	-
0.5227	1225	0.5795	-
0.5249	1230	0.652	-
0.5270	1235	0.6023	-
0.5291	1240	0.6742	-
0.5313	1245	0.5938	-
0.5334	1250	0.5831	-
0.5355	1255	0.6278	-
0.5377	1260	0.5228	-
0.5398	1265	0.5514	-
0.5419	1270	0.6121	-
0.5441	1275	0.5538	-
0.5462	1280	0.5912	-
0.5483	1285	0.5777	-
0.5505	1290	0.5657	-
0.5526	1295	0.6132	-
0.5547	1300	0.5407	-
0.5569	1305	0.5939	-
0.5590	1310	0.6412	-
0.5611	1315	0.5422	-
0.5633	1320	0.6404	-
0.5654	1325	0.6208	-
0.5675	1330	0.5722	-
0.5697	1335	0.5092	-
0.5718	1340	0.6061	-
0.5739	1345	0.6441	-
0.5761	1350	0.5957	-
0.5782	1355	0.6249	-
0.5803	1360	0.6002	-
0.5825	1365	0.6096	-
0.5846	1370	0.5466	-
0.5867	1375	0.5664	-
0.5889	1380	0.6218	-
0.5910	1385	0.513	-
0.5931	1390	0.6036	-
0.5953	1395	0.5427	-
0.5974	1400	0.4911	-
0.5995	1405	0.5789	-
0.6017	1410	0.5746	-
0.6038	1415	0.6073	-
0.6059	1420	0.5064	-
0.6081	1425	0.5846	-
0.6102	1430	0.5368	-
0.6123	1435	0.5735	-
0.6145	1440	0.5119	-
0.6166	1445	0.5429	-
0.6187	1450	0.6141	-
0.6209	1455	0.5224	-
0.6230	1460	0.5656	-
0.6251	1465	0.567	-
0.6273	1470	0.5517	-
0.6294	1475	0.6311	-
0.6315	1480	0.5862	-
0.6337	1485	0.5699	-
0.6358	1490	0.574	-
0.6379	1495	0.5611	-
0.6401	1500	0.4966	-
0.6422	1505	0.5305	-
0.6443	1510	0.5228	-
0.6465	1515	0.5816	-
0.6486	1520	0.5538	-
0.6507	1525	0.4972	-
0.6529	1530	0.5677	-
0.6550	1535	0.5891	-
0.6571	1540	0.5463	-
0.6593	1545	0.6041	-
0.6614	1550	0.7248	-
0.6635	1555	0.6366	-
0.6657	1560	0.6056	-
0.6678	1565	0.5468	-
0.6699	1570	0.5638	-
0.6721	1575	0.5833	-
0.6742	1580	0.6128	-
0.6763	1585	0.5525	-
0.6785	1590	0.5735	-
0.6806	1595	0.5409	-
0.6827	1600	0.545	-
0.6849	1605	0.4922	-
0.6870	1610	0.6188	-
0.6891	1615	0.5705	-
0.6913	1620	0.5205	-
0.6934	1625	0.5568	-
0.6955	1630	0.5565	-
0.6977	1635	0.5122	-
0.6998	1640	0.5414	-
0.7019	1645	0.5126	-
0.7041	1650	0.5426	-
0.7062	1655	0.5764	-
0.7083	1660	0.5159	-
0.7105	1665	0.576	-
0.7126	1670	0.5781	-
0.7147	1675	0.5164	-
0.7169	1680	0.5278	-
0.7190	1685	0.5667	-
0.7211	1690	0.4889	-
0.7233	1695	0.5163	-
0.7254	1700	0.523	-
0.7275	1705	0.5503	-
0.7297	1710	0.4932	-
0.7318	1715	0.4973	-
0.7339	1720	0.5625	-
0.7361	1725	0.5335	-
0.7382	1730	0.4446	-
0.7403	1735	0.5377	-
0.7425	1740	0.5036	-
0.7446	1745	0.6074	-
0.7467	1750	0.5982	-
0.7489	1755	0.5762	-
0.7510	1760	0.5379	-
0.7531	1765	0.5702	-
0.7553	1770	0.4272	-
0.7574	1775	0.4805	-
0.7595	1780	0.4963	-
0.7617	1785	0.4846	-
0.7638	1790	0.584	-
0.7659	1795	0.5158	-
0.7681	1800	0.5821	-
0.7702	1805	0.4609	-
0.7723	1810	0.4335	-
0.7745	1815	0.5672	-
0.7766	1820	0.467	-
0.7787	1825	0.4924	-
0.7809	1830	0.4786	-
0.7830	1835	0.5365	-
0.7852	1840	0.5199	-
0.7873	1845	0.5243	-
0.7894	1850	0.4685	-
0.7916	1855	0.6146	-
0.7937	1860	0.5446	-
0.7958	1865	0.5278	-
0.7980	1870	0.5452	-
0.8001	1875	0.5923	-
0.8022	1880	0.4944	-
0.8044	1885	0.5447	-
0.8065	1890	0.5575	-
0.8086	1895	0.5922	-
0.8108	1900	0.498	-
0.8129	1905	0.4965	-
0.8150	1910	0.5762	-
0.8172	1915	0.5036	-
0.8193	1920	0.5089	-
0.8214	1925	0.5219	-
0.8236	1930	0.5821	-
0.8257	1935	0.4672	-
0.8278	1940	0.4639	-
0.8300	1945	0.518	-
0.8321	1950	0.4295	-
0.8342	1955	0.4736	-
0.8364	1960	0.5154	-
0.8385	1965	0.598	-
0.8406	1970	0.52	-
0.8428	1975	0.438	-
0.8449	1980	0.4639	-
0.8470	1985	0.5121	-
0.8492	1990	0.4759	-
0.8513	1995	0.5611	-
0.8534	2000	0.5262	-
0.8556	2005	0.5217	-
0.8577	2010	0.4964	-
0.8598	2015	0.4598	-
0.8620	2020	0.525	-
0.8641	2025	0.4956	-
0.8662	2030	0.5389	-
0.8684	2035	0.4701	-
0.8705	2040	0.5139	-
0.8726	2045	0.4604	-
0.8748	2050	0.5102	-
0.8769	2055	0.4905	-
0.8790	2060	0.4595	-
0.8812	2065	0.5007	-
0.8833	2070	0.438	-
0.8854	2075	0.4689	-
0.8876	2080	0.4569	-
0.8897	2085	0.5259	-
0.8918	2090	0.4039	-
0.8940	2095	0.4846	-
0.8961	2100	0.4887	-
0.8982	2105	0.5263	-
0.9004	2110	0.4924	-
0.9025	2115	0.4973	-
0.9046	2120	0.4772	-
0.9068	2125	0.5006	-
0.9089	2130	0.4475	-
0.9110	2135	0.497	-
0.9132	2140	0.4469	-
0.9153	2145	0.4795	-
0.9174	2150	0.5059	-
0.9196	2155	0.4438	-
0.9217	2160	0.4385	-
0.9238	2165	0.5495	-
0.9260	2170	0.45	-
0.9281	2175	0.5123	-
0.9302	2180	0.4433	-
0.9324	2185	0.4567	-
0.9345	2190	0.4398	-
0.9366	2195	0.4711	-
0.9388	2200	0.4646	-
0.9409	2205	0.4591	-
0.9430	2210	0.4937	-
0.9452	2215	0.4774	-
0.9473	2220	0.4905	-
0.9494	2225	0.4965	-
0.9516	2230	0.4581	-
0.9537	2235	0.4821	-
0.9558	2240	0.4719	-
0.9580	2245	0.522	-
0.9601	2250	0.5162	-
0.9622	2255	0.5248	-
0.9644	2260	0.5118	-
0.9665	2265	0.4359	-
0.9686	2270	0.5012	-
0.9708	2275	0.4344	-
0.9729	2280	0.5507	-
0.9750	2285	0.4657	-
0.9772	2290	0.471	-
0.9793	2295	0.4363	-
0.9814	2300	0.4178	-
0.9836	2305	0.4703	-
0.9857	2310	0.4736	-
0.9878	2315	0.4432	-
0.9900	2320	0.4397	-
0.9921	2325	0.4539	-
0.9942	2330	0.4944	-
0.9964	2335	0.553	-
0.9985	2340	0.5004	-
1.0	2344	-	0.2073
1.0004	2345	0.4931	-
1.0026	2350	0.5427	-
1.0047	2355	0.5448	-
1.0068	2360	0.4535	-
1.0090	2365	0.4225	-
1.0111	2370	0.4968	-
1.0132	2375	0.487	-
1.0154	2380	0.554	-
1.0175	2385	0.5981	-
1.0196	2390	0.4725	-
1.0218	2395	0.595	-
1.0239	2400	0.5523	-
1.0260	2405	0.5019	-
1.0282	2410	0.4736	-
1.0303	2415	0.5149	-
1.0324	2420	0.4451	-
1.0346	2425	0.437	-
1.0367	2430	0.5862	-
1.0388	2435	0.488	-
1.0410	2440	0.4786	-
1.0431	2445	0.5068	-
1.0452	2450	0.4296	-
1.0474	2455	0.4776	-
1.0495	2460	0.4776	-
1.0516	2465	0.5713	-
1.0538	2470	0.4727	-
1.0559	2475	0.5128	-
1.0580	2480	0.5382	-
1.0602	2485	0.4477	-
1.0623	2490	0.4534	-
1.0644	2495	0.5227	-
1.0666	2500	0.4626	-
1.0687	2505	0.5692	-
1.0708	2510	0.4777	-
1.0730	2515	0.4565	-
1.0751	2520	0.5691	-
1.0772	2525	0.6618	-
1.0794	2530	0.513	-
1.0815	2535	0.4544	-
1.0836	2540	0.476	-
1.0858	2545	0.4708	-
1.0879	2550	0.5527	-
1.0900	2555	0.4507	-
1.0922	2560	0.5308	-
1.0943	2565	0.461	-
1.0964	2570	0.4482	-
1.0986	2575	0.4656	-
1.1007	2580	0.5048	-
1.1028	2585	0.4857	-
1.1050	2590	0.5365	-
1.1071	2595	0.4044	-
1.1092	2600	0.4476	-
1.1114	2605	0.6451	-
1.1135	2610	0.4513	-
1.1156	2615	0.3723	-
1.1178	2620	0.4665	-
1.1199	2625	0.522	-
1.1220	2630	0.5245	-
1.1242	2635	0.4309	-
1.1263	2640	0.4553	-
1.1284	2645	0.4862	-
1.1306	2650	0.5337	-
1.1327	2655	0.4743	-
1.1348	2660	0.4623	-
1.1370	2665	0.5064	-
1.1391	2670	0.503	-
1.1412	2675	0.5103	-
1.1434	2680	0.4481	-
1.1455	2685	0.4804	-
1.1476	2690	0.4587	-
1.1498	2695	0.5718	-
1.1519	2700	0.428	-
1.1540	2705	0.4927	-
1.1562	2710	0.4218	-
1.1583	2715	0.4965	-
1.1604	2720	0.4098	-
1.1626	2725	0.4597	-
1.1647	2730	0.4558	-
1.1668	2735	0.4923	-
1.1690	2740	0.5261	-
1.1711	2745	0.5161	-
1.1732	2750	0.524	-
1.1754	2755	0.4531	-
1.1775	2760	0.5125	-
1.1796	2765	0.5122	-
1.1818	2770	0.4124	-
1.1839	2775	0.7222	-
1.1860	2780	0.4534	-
1.1882	2785	0.5466	-
1.1903	2790	0.5265	-
1.1924	2795	0.4826	-
1.1946	2800	0.3948	-
1.1967	2805	0.5772	-
1.1988	2810	0.4961	-
1.2010	2815	0.4958	-
1.2031	2820	0.4373	-
1.2052	2825	0.4558	-
1.2074	2830	0.5777	-
1.2095	2835	0.4237	-
1.2116	2840	0.435	-
1.2138	2845	0.5261	-
1.2159	2850	0.486	-
1.2180	2855	0.4067	-
1.2202	2860	0.5407	-
1.2223	2865	0.4533	-
1.2245	2870	0.4071	-
1.2266	2875	0.591	-
1.2287	2880	0.6181	-
1.2309	2885	0.4433	-
1.2330	2890	0.4551	-
1.2351	2895	0.4241	-
1.2373	2900	0.4712	-
1.2394	2905	0.4937	-
1.2415	2910	0.5162	-
1.2437	2915	0.4604	-
1.2458	2920	0.5275	-
1.2479	2925	0.4295	-
1.2501	2930	0.3889	-
1.2522	2935	0.4719	-
1.2543	2940	0.4852	-
1.2565	2945	0.4006	-
1.2586	2950	0.4696	-
1.2607	2955	0.426	-
1.2629	2960	0.4443	-
1.2650	2965	0.4202	-
1.2671	2970	0.4982	-
1.2693	2975	0.4503	-
1.2714	2980	0.4007	-
1.2735	2985	0.4376	-
1.2757	2990	0.4763	-
1.2778	2995	0.4709	-
1.2799	3000	0.5067	-
1.2821	3005	0.4276	-
1.2842	3010	0.4038	-
1.2863	3015	0.5393	-
1.2885	3020	0.4085	-
1.2906	3025	0.415	-
1.2927	3030	0.5042	-
1.2949	3035	0.6117	-
1.2970	3040	0.4961	-
1.2991	3045	0.4577	-
1.3013	3050	0.4806	-
1.3034	3055	0.5106	-
1.3055	3060	0.4831	-
1.3077	3065	0.463	-
1.3098	3070	0.562	-
1.3119	3075	0.407	-
1.3141	3080	0.371	-
1.3162	3085	0.4977	-
1.3183	3090	0.4592	-
1.3205	3095	0.4373	-
1.3226	3100	0.41	-
1.3247	3105	0.4909	-
1.3269	3110	0.4441	-
1.3290	3115	0.4104	-
1.3311	3120	0.5863	-
1.3333	3125	0.4861	-
1.3354	3130	0.448	-
1.3375	3135	0.4856	-
1.3397	3140	0.4659	-
1.3418	3145	0.4927	-
1.3439	3150	0.3763	-
1.3461	3155	0.3982	-
1.3482	3160	0.4306	-
1.3503	3165	0.4321	-
1.3525	3170	0.4122	-
1.3546	3175	0.4976	-
1.3567	3180	0.468	-
1.3589	3185	0.5257	-
1.3610	3190	0.4232	-
1.3631	3195	0.4477	-
1.3653	3200	0.4556	-
1.3674	3205	0.5006	-
1.3695	3210	0.4101	-
1.3717	3215	0.3969	-
1.3738	3220	0.3966	-
1.3759	3225	0.494	-
1.3781	3230	0.4584	-
1.3802	3235	0.5692	-
1.3823	3240	0.44	-
1.3845	3245	0.4181	-
1.3866	3250	0.486	-
1.3887	3255	0.4839	-
1.3909	3260	0.4425	-
1.3930	3265	0.4286	-
1.3951	3270	0.4605	-
1.3973	3275	0.4891	-
1.3994	3280	0.4053	-
1.4015	3285	0.4438	-
1.4037	3290	0.4735	-
1.4058	3295	0.5695	-
1.4079	3300	0.464	-
1.4101	3305	0.4787	-
1.4122	3310	0.4302	-
1.4143	3315	0.4341	-
1.4165	3320	0.367	-
1.4186	3325	0.4708	-
1.4207	3330	0.4751	-
1.4229	3335	0.4474	-
1.4250	3340	0.4168	-
1.4271	3345	0.4383	-
1.4293	3350	0.438	-
1.4314	3355	0.4385	-
1.4335	3360	0.4577	-
1.4357	3365	0.435	-
1.4378	3370	0.4775	-
1.4399	3375	0.4046	-
1.4421	3380	0.4921	-
1.4442	3385	0.4148	-
1.4463	3390	0.4061	-
1.4485	3395	0.3859	-
1.4506	3400	0.5035	-
1.4527	3405	0.4537	-
1.4549	3410	0.4655	-
1.4570	3415	0.3928	-
1.4591	3420	0.4426	-
1.4613	3425	0.506	-
1.4634	3430	0.3835	-
1.4655	3435	0.5111	-
1.4677	3440	0.3948	-
1.4698	3445	0.4282	-
1.4719	3450	0.4408	-
1.4741	3455	0.4192	-
1.4762	3460	0.4471	-
1.4783	3465	0.4319	-
1.4805	3470	0.4367	-
1.4826	3475	0.4216	-
1.4847	3480	0.4344	-
1.4869	3485	0.4263	-
1.4890	3490	0.4619	-
1.4911	3495	0.5105	-
1.4933	3500	0.4722	-
1.4954	3505	0.4152	-
1.4975	3510	0.4017	-
1.4997	3515	0.4496	-
1.5018	3520	0.4965	-
1.5039	3525	0.4009	-
1.5061	3530	0.5115	-
1.5082	3535	0.4007	-
1.5103	3540	0.4195	-
1.5125	3545	0.4412	-
1.5146	3550	0.5177	-
1.5167	3555	0.4553	-
1.5189	3560	0.4378	-
1.5210	3565	0.4296	-
1.5231	3570	0.406	-
1.5253	3575	0.4636	-
1.5274	3580	0.4244	-
1.5295	3585	0.5027	-
1.5317	3590	0.4284	-
1.5338	3595	0.3893	-
1.5360	3600	0.515	-
1.5381	3605	0.3867	-
1.5402	3610	0.5842	-
1.5424	3615	0.3753	-
1.5445	3620	0.4834	-
1.5466	3625	0.4547	-
1.5488	3630	0.4224	-
1.5509	3635	0.4341	-
1.5530	3640	0.4038	-
1.5552	3645	0.3606	-
1.5573	3650	0.4427	-
1.5594	3655	0.4919	-
1.5616	3660	0.362	-
1.5637	3665	0.4261	-
1.5658	3670	0.4269	-
1.5680	3675	0.4291	-
1.5701	3680	0.3967	-
1.5722	3685	0.4645	-
1.5744	3690	0.4052	-
1.5765	3695	0.4627	-
1.5786	3700	0.3856	-
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1.5829	3710	0.4104	-
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1.5872	3720	0.5145	-
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1.5914	3730	0.3861	-
1.5936	3735	0.4274	-
1.5957	3740	0.4857	-
1.5978	3745	0.5526	-
1.6000	3750	0.4052	-
1.6021	3755	0.4087	-
1.6042	3760	0.3831	-
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1.6085	3770	0.4158	-
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1.6170	3790	0.4613	-
1.6192	3795	0.4321	-
1.6213	3800	0.4867	-
1.6234	3805	0.4404	-
1.6256	3810	0.4435	-
1.6277	3815	0.4115	-
1.6298	3820	0.4811	-
1.6320	3825	0.4128	-
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1.6384	3840	0.3844	-
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1.6426	3850	0.3869	-
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1.6490	3865	0.4017	-
1.6512	3870	0.3972	-
1.6533	3875	0.4669	-
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1.6597	3890	0.4411	-
1.6618	3895	0.4277	-
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1.6661	3905	0.4967	-
1.6682	3910	0.371	-
1.6704	3915	0.4501	-
1.6725	3920	0.4275	-
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1.6789	3935	0.4142	-
1.6810	3940	0.4424	-
1.6832	3945	0.4194	-
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1.6874	3955	0.3881	-
1.6896	3960	0.3924	-
1.6917	3965	0.4241	-
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1.6960	3975	0.4394	-
1.6981	3980	0.4414	-
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1.7024	3990	0.4267	-
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1.7066	4000	0.4125	-
1.7088	4005	0.4284	-
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1.7216	4035	0.4214	-
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1.7280	4050	0.4346	-
1.7301	4055	0.4518	-
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1.7344	4065	0.4222	-
1.7365	4070	0.4169	-
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1.7408	4080	0.3874	-
1.7429	4085	0.4318	-
1.7450	4090	0.4566	-
1.7472	4095	0.4351	-
1.7493	4100	0.5365	-
1.7514	4105	0.3924	-
1.7536	4110	0.4072	-
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1.7578	4120	0.3614	-
1.7600	4125	0.4256	-
1.7621	4130	0.4156	-
1.7642	4135	0.4392	-
1.7664	4140	0.3626	-
1.7685	4145	0.4007	-
1.7706	4150	0.4329	-
1.7728	4155	0.4734	-
1.7749	4160	0.3963	-
1.7770	4165	0.3708	-
1.7792	4170	0.385	-
1.7813	4175	0.3566	-
1.7834	4180	0.4153	-
1.7856	4185	0.3932	-
1.7877	4190	0.421	-
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1.7920	4200	0.4133	-
1.7941	4205	0.4671	-
1.7962	4210	0.4151	-
1.7984	4215	0.3906	-
1.8005	4220	0.38	-
1.8026	4225	0.4217	-
1.8048	4230	0.391	-
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1.8090	4240	0.4448	-
1.8112	4245	0.4197	-
1.8133	4250	0.4442	-
1.8154	4255	0.4299	-
1.8176	4260	0.3981	-
1.8197	4265	0.4499	-
1.8218	4270	0.4081	-
1.8240	4275	0.389	-
1.8261	4280	0.4191	-
1.8282	4285	0.3995	-
1.8304	4290	0.3994	-
1.8325	4295	0.4107	-
1.8346	4300	0.3564	-
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1.8389	4310	0.3079	-
1.8410	4315	0.3903	-
1.8432	4320	0.4295	-
1.8453	4325	0.4108	-
1.8475	4330	0.3975	-
1.8496	4335	0.36	-
1.8517	4340	0.3914	-
1.8539	4345	0.3827	-
1.8560	4350	0.4142	-
1.8581	4355	0.3415	-
1.8603	4360	0.3879	-
1.8624	4365	0.4991	-
1.8645	4370	0.3334	-
1.8667	4375	0.3978	-
1.8688	4380	0.4217	-
1.8709	4385	0.4304	-
1.8731	4390	0.3924	-
1.8752	4395	0.4051	-
1.8773	4400	0.4178	-
1.8795	4405	0.3778	-
1.8816	4410	0.3979	-
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1.8859	4420	0.3896	-
1.8880	4425	0.4409	-
1.8901	4430	0.4299	-
1.8923	4435	0.399	-
1.8944	4440	0.4148	-
1.8965	4445	0.4501	-
1.8987	4450	0.3909	-
1.9008	4455	0.4316	-
1.9029	4460	0.3662	-
1.9051	4465	0.4446	-
1.9072	4470	0.3931	-
1.9093	4475	0.3947	-
1.9115	4480	0.3415	-
1.9136	4485	0.3661	-
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1.9179	4495	0.3918	-
1.9200	4500	0.3459	-
1.9221	4505	0.3944	-
1.9243	4510	0.3727	-
1.9264	4515	0.3956	-
1.9285	4520	0.4233	-
1.9307	4525	0.3929	-
1.9328	4530	0.4206	-
1.9349	4535	0.3835	-
1.9371	4540	0.459	-
1.9392	4545	0.4094	-
1.9413	4550	0.3996	-
1.9435	4555	0.4309	-
1.9456	4560	0.3579	-
1.9477	4565	0.4028	-
1.9499	4570	0.3659	-
1.9520	4575	0.3697	-
1.9541	4580	0.3926	-
1.9563	4585	0.3827	-
1.9584	4590	0.4603	-
1.9605	4595	0.4473	-
1.9627	4600	0.4027	-
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1.9669	4610	0.4502	-
1.9691	4615	0.3708	-
1.9712	4620	0.3747	-
1.9733	4625	0.3385	-
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1.9776	4635	0.4506	-
1.9797	4640	0.3889	-
1.9819	4645	0.4104	-
1.9840	4650	0.4283	-
1.9861	4655	0.3305	-
1.9883	4660	0.393	-
1.9904	4665	0.416	-
1.9925	4670	0.3541	-
1.9947	4675	0.4027	-
1.9968	4680	0.3685	-
1.9989	4685	0.3796	-
2.0	4688	-	0.1772
2.0009	4690	0.3541	-
2.0030	4695	0.4464	-
2.0051	4700	0.3864	-
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2.0115	4715	0.4576	-
2.0137	4720	0.3428	-
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2.0222	4740	0.3894	-
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2.0265	4750	0.3693	-
2.0286	4755	0.3899	-
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2.0329	4765	0.4794	-
2.0350	4770	0.4105	-
2.0371	4775	0.4166	-
2.0393	4780	0.3861	-
2.0414	4785	0.4576	-
2.0435	4790	0.4476	-
2.0457	4795	0.4411	-
2.0478	4800	0.3745	-
2.0499	4805	0.5016	-
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2.0542	4815	0.4508	-
2.0563	4820	0.4173	-
2.0585	4825	0.4008	-
2.0606	4830	0.4128	-
2.0627	4835	0.4003	-
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2.0670	4845	0.3549	-
2.0691	4850	0.4706	-
2.0713	4855	0.398	-
2.0734	4860	0.3775	-
2.0755	4865	0.4547	-
2.0777	4870	0.4048	-
2.0798	4875	0.4562	-
2.0819	4880	0.4327	-
2.0841	4885	0.49	-
2.0862	4890	0.3935	-
2.0883	4895	0.3395	-
2.0905	4900	0.4126	-
2.0926	4905	0.5132	-
2.0947	4910	0.4168	-
2.0969	4915	0.4265	-
2.0990	4920	0.4056	-
2.1011	4925	0.391	-
2.1033	4930	0.452	-
2.1054	4935	0.3674	-
2.1075	4940	0.4484	-
2.1097	4945	0.4447	-
2.1118	4950	0.4224	-
2.1139	4955	0.4074	-
2.1161	4960	0.4031	-
2.1182	4965	0.4308	-
2.1203	4970	0.5257	-
2.1225	4975	0.4075	-
2.1246	4980	0.3787	-
2.1267	4985	0.415	-
2.1289	4990	0.4389	-
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2.1331	5000	0.4361	-
2.1353	5005	0.3702	-
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2.1395	5015	0.4284	-
2.1417	5020	0.3721	-
2.1438	5025	0.3834	-
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2.1481	5035	0.3983	-
2.1502	5040	0.3478	-
2.1523	5045	0.4092	-
2.1545	5050	0.4197	-
2.1566	5055	0.38	-
2.1587	5060	0.3638	-
2.1609	5065	0.4944	-
2.1630	5070	0.4456	-
2.1651	5075	0.4251	-
2.1673	5080	0.4029	-
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2.1715	5090	0.3824	-
2.1737	5095	0.4084	-
2.1758	5100	0.4154	-
2.1779	5105	0.3933	-
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2.1822	5115	0.3911	-
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2.1865	5125	0.3823	-
2.1886	5130	0.4173	-
2.1907	5135	0.3648	-
2.1929	5140	0.4877	-
2.1950	5145	0.4069	-
2.1971	5150	0.447	-
2.1993	5155	0.4186	-
2.2014	5160	0.4088	-
2.2035	5165	0.5046	-
2.2057	5170	0.409	-
2.2078	5175	0.4102	-
2.2099	5180	0.4145	-
2.2121	5185	0.4229	-
2.2142	5190	0.4613	-
2.2163	5195	0.413	-
2.2185	5200	0.3725	-
2.2206	5205	0.4417	-
2.2227	5210	0.3696	-
2.2249	5215	0.4294	-
2.2270	5220	0.4408	-
2.2291	5225	0.4007	-
2.2313	5230	0.4927	-
2.2334	5235	0.3488	-
2.2355	5240	0.4898	-
2.2377	5245	0.43	-
2.2398	5250	0.4281	-
2.2419	5255	0.4307	-
2.2441	5260	0.4487	-
2.2462	5265	0.4379	-
2.2483	5270	0.3901	-
2.2505	5275	0.4434	-
2.2526	5280	0.3758	-
2.2547	5285	0.3693	-
2.2569	5290	0.3965	-
2.2590	5295	0.3508	-
2.2611	5300	0.4771	-
2.2633	5305	0.3601	-
2.2654	5310	0.4161	-
2.2675	5315	0.4813	-
2.2697	5320	0.3715	-
2.2718	5325	0.3873	-
2.2739	5330	0.4092	-
2.2761	5335	0.4073	-
2.2782	5340	0.3975	-
2.2803	5345	0.3485	-
2.2825	5350	0.3857	-
2.2846	5355	0.3158	-
2.2868	5360	0.4476	-
2.2889	5365	0.4784	-
2.2910	5370	0.4499	-
2.2932	5375	0.3852	-
2.2953	5380	0.4927	-
2.2974	5385	0.5091	-
2.2996	5390	0.4201	-
2.3017	5395	0.406	-
2.3038	5400	0.3989	-
2.3060	5405	0.4134	-
2.3081	5410	0.4595	-
2.3102	5415	0.4157	-
2.3124	5420	0.4703	-
2.3145	5425	0.4543	-
2.3166	5430	0.4207	-
2.3188	5435	0.4049	-
2.3209	5440	0.4193	-
2.3230	5445	0.3917	-
2.3252	5450	0.4742	-
2.3273	5455	0.3965	-
2.3294	5460	0.3876	-
2.3316	5465	0.4568	-
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2.3358	5475	0.3959	-
2.3380	5480	0.4605	-
2.3401	5485	0.3838	-
2.3422	5490	0.4498	-
2.3444	5495	0.4091	-
2.3465	5500	0.3803	-
2.3486	5505	0.3776	-
2.3508	5510	0.4464	-
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2.3550	5520	0.4251	-
2.3572	5525	0.3529	-
2.3593	5530	0.3897	-
2.3614	5535	0.3916	-
2.3636	5540	0.3637	-
2.3657	5545	0.387	-
2.3678	5550	0.3847	-
2.3700	5555	0.4186	-
2.3721	5560	0.4446	-
2.3742	5565	0.3906	-
2.3764	5570	0.3741	-
2.3785	5575	0.385	-
2.3806	5580	0.3454	-
2.3828	5585	0.4328	-
2.3849	5590	0.3027	-
2.3870	5595	0.3366	-
2.3892	5600	0.4076	-
2.3913	5605	0.3898	-
2.3934	5610	0.3639	-
2.3956	5615	0.4414	-
2.3977	5620	0.3376	-
2.3998	5625	0.3839	-
2.4020	5630	0.3679	-
2.4041	5635	0.3895	-
2.4062	5640	0.3644	-
2.4084	5645	0.4035	-
2.4105	5650	0.5727	-
2.4126	5655	0.4091	-
2.4148	5660	0.4028	-
2.4169	5665	0.4611	-
2.4190	5670	0.4054	-
2.4212	5675	0.489	-
2.4233	5680	0.3478	-
2.4254	5685	0.3713	-
2.4276	5690	0.4119	-
2.4297	5695	0.4012	-
2.4318	5700	0.3511	-
2.4340	5705	0.466	-
2.4361	5710	0.4053	-
2.4382	5715	0.3865	-
2.4404	5720	0.3319	-
2.4425	5725	0.3993	-
2.4446	5730	0.4318	-
2.4468	5735	0.4326	-
2.4489	5740	0.3337	-
2.4510	5745	0.3598	-
2.4532	5750	0.3828	-
2.4553	5755	0.3431	-
2.4574	5760	0.4396	-
2.4596	5765	0.3572	-
2.4617	5770	0.3988	-
2.4638	5775	0.4699	-
2.4660	5780	0.4798	-
2.4681	5785	0.4607	-
2.4702	5790	0.3383	-
2.4724	5795	0.3431	-
2.4745	5800	0.4159	-
2.4766	5805	0.5409	-
2.4788	5810	0.4068	-
2.4809	5815	0.4264	-
2.4830	5820	0.4537	-
2.4852	5825	0.3723	-
2.4873	5830	0.3822	-
2.4894	5835	0.3885	-
2.4916	5840	0.3656	-
2.4937	5845	0.4383	-
2.4958	5850	0.3617	-
2.4980	5855	0.3459	-
2.5001	5860	0.4128	-
2.5022	5865	0.347	-
2.5044	5870	0.414	-
2.5065	5875	0.3732	-
2.5086	5880	0.3981	-
2.5108	5885	0.3326	-
2.5129	5890	0.4446	-
2.5150	5895	0.3966	-
2.5172	5900	0.3961	-
2.5193	5905	0.3975	-
2.5214	5910	0.4075	-
2.5236	5915	0.3733	-
2.5257	5920	0.4036	-
2.5278	5925	0.3915	-
2.5300	5930	0.4529	-
2.5321	5935	0.3848	-
2.5342	5940	0.4371	-
2.5364	5945	0.374	-
2.5385	5950	0.4176	-
2.5406	5955	0.3825	-
2.5428	5960	0.3904	-
2.5449	5965	0.3464	-
2.5470	5970	0.3689	-
2.5492	5975	0.4211	-
2.5513	5980	0.3731	-
2.5534	5985	0.3762	-
2.5556	5990	0.3827	-
2.5577	5995	0.3539	-
2.5598	6000	0.4552	-
2.5620	6005	0.3902	-
2.5641	6010	0.3735	-
2.5662	6015	0.4999	-
2.5684	6020	0.3277	-
2.5705	6025	0.3806	-
2.5726	6030	0.423	-
2.5748	6035	0.4111	-
2.5769	6040	0.3823	-
2.5790	6045	0.4564	-
2.5812	6050	0.4409	-
2.5833	6055	0.4298	-
2.5854	6060	0.4348	-
2.5876	6065	0.3997	-
2.5897	6070	0.4664	-
2.5918	6075	0.4231	-
2.5940	6080	0.3717	-
2.5961	6085	0.4009	-
2.5983	6090	0.3993	-
2.6004	6095	0.3631	-
2.6025	6100	0.405	-
2.6047	6105	0.3838	-
2.6068	6110	0.4622	-
2.6089	6115	0.5596	-
2.6111	6120	0.4108	-
2.6132	6125	0.4462	-
2.6153	6130	0.3573	-
2.6175	6135	0.4009	-
2.6196	6140	0.4118	-
2.6217	6145	0.3667	-
2.6239	6150	0.3552	-
2.6260	6155	0.4853	-
2.6281	6160	0.4286	-
2.6303	6165	0.4329	-
2.6324	6170	0.375	-
2.6345	6175	0.4039	-
2.6367	6180	0.4411	-
2.6388	6185	0.3401	-
2.6409	6190	0.4211	-
2.6431	6195	0.4059	-
2.6452	6200	0.3441	-
2.6473	6205	0.3342	-
2.6495	6210	0.3825	-
2.6516	6215	0.4421	-
2.6537	6220	0.3765	-
2.6559	6225	0.3601	-
2.6580	6230	0.397	-
2.6601	6235	0.4327	-
2.6623	6240	0.3815	-
2.6644	6245	0.4483	-
2.6665	6250	0.3829	-
2.6687	6255	0.4072	-
2.6708	6260	0.3995	-
2.6729	6265	0.3702	-
2.6751	6270	0.3905	-
2.6772	6275	0.4461	-
2.6793	6280	0.3363	-
2.6815	6285	0.3668	-
2.6836	6290	0.3367	-
2.6857	6295	0.3845	-
2.6879	6300	0.3561	-
2.6900	6305	0.3932	-
2.6921	6310	0.4074	-
2.6943	6315	0.3642	-
2.6964	6320	0.3365	-
2.6985	6325	0.3501	-
2.7007	6330	0.3824	-
2.7028	6335	0.4223	-
2.7049	6340	0.3694	-
2.7071	6345	0.4587	-
2.7092	6350	0.3643	-
2.7113	6355	0.3443	-
2.7135	6360	0.3883	-
2.7156	6365	0.3858	-
2.7177	6370	0.3656	-
2.7199	6375	0.3921	-
2.7220	6380	0.3966	-
2.7241	6385	0.4512	-
2.7263	6390	0.36	-
2.7284	6395	0.4329	-
2.7305	6400	0.3722	-
2.7327	6405	0.3646	-
2.7348	6410	0.393	-
2.7369	6415	0.4266	-
2.7391	6420	0.363	-
2.7412	6425	0.3979	-
2.7433	6430	0.379	-
2.7455	6435	0.3853	-
2.7476	6440	0.3632	-
2.7497	6445	0.4081	-
2.7519	6450	0.4033	-
2.7540	6455	0.3946	-
2.7561	6460	0.3884	-
2.7583	6465	0.4043	-
2.7604	6470	0.3765	-
2.7625	6475	0.4379	-
2.7647	6480	0.3688	-
2.7668	6485	0.351	-
2.7689	6490	0.3465	-
2.7711	6495	0.424	-
2.7732	6500	0.3487	-
2.7753	6505	0.343	-
2.7775	6510	0.3745	-
2.7796	6515	0.3482	-
2.7817	6520	0.419	-
2.7839	6525	0.4107	-
2.7860	6530	0.3103	-
2.7881	6535	0.3836	-
2.7903	6540	0.3736	-
2.7924	6545	0.3926	-
2.7945	6550	0.384	-
2.7967	6555	0.3862	-
2.7988	6560	0.41	-
2.8009	6565	0.4223	-
2.8031	6570	0.385	-
2.8052	6575	0.3875	-
2.8073	6580	0.3847	-
2.8095	6585	0.4115	-
2.8116	6590	0.3578	-
2.8137	6595	0.3868	-
2.8159	6600	0.3581	-
2.8180	6605	0.3467	-
2.8201	6610	0.3521	-
2.8223	6615	0.3841	-
2.8244	6620	0.3963	-
2.8265	6625	0.4293	-
2.8287	6630	0.4127	-
2.8308	6635	0.3977	-
2.8329	6640	0.3416	-
2.8351	6645	0.4633	-
2.8372	6650	0.3987	-
2.8393	6655	0.3409	-
2.8415	6660	0.4259	-
2.8436	6665	0.3581	-
2.8457	6670	0.439	-
2.8479	6675	0.4366	-
2.8500	6680	0.3568	-
2.8521	6685	0.4044	-
2.8543	6690	0.3394	-
2.8564	6695	0.3631	-
2.8585	6700	0.3771	-
2.8607	6705	0.3673	-
2.8628	6710	0.431	-
2.8649	6715	0.3884	-
2.8671	6720	0.3317	-
2.8692	6725	0.4444	-
2.8713	6730	0.3629	-
2.8735	6735	0.3985	-
2.8756	6740	0.3658	-
2.8777	6745	0.4409	-
2.8799	6750	0.3299	-
2.8820	6755	0.4275	-
2.8841	6760	0.398	-
2.8863	6765	0.4017	-
2.8884	6770	0.4163	-
2.8905	6775	0.4025	-
2.8927	6780	0.4233	-
2.8948	6785	0.4032	-
2.8969	6790	0.3724	-
2.8991	6795	0.3426	-
2.9012	6800	0.4207	-
2.9033	6805	0.394	-
2.9055	6810	0.3566	-
2.9076	6815	0.3491	-
2.9098	6820	0.4027	-
2.9119	6825	0.3844	-
2.9140	6830	0.3469	-
2.9162	6835	0.368	-
2.9183	6840	0.3673	-
2.9204	6845	0.3668	-
2.9226	6850	0.3892	-
2.9247	6855	0.3819	-
2.9268	6860	0.3969	-
2.9290	6865	0.3556	-
2.9311	6870	0.3972	-
2.9332	6875	0.3881	-
2.9354	6880	0.3569	-
2.9375	6885	0.3775	-
2.9396	6890	0.3723	-
2.9418	6895	0.4026	-
2.9439	6900	0.3598	-
2.9460	6905	0.396	-
2.9482	6910	0.4212	-
2.9503	6915	0.3016	-
2.9524	6920	0.3691	-
2.9546	6925	0.4057	-
2.9567	6930	0.3758	-
2.9588	6935	0.4074	-
2.9610	6940	0.3908	-
2.9631	6945	0.3797	-
2.9652	6950	0.3643	-
2.9674	6955	0.3865	-
2.9695	6960	0.3911	-
2.9716	6965	0.3594	-
2.9738	6970	0.3772	-
2.9759	6975	0.405	-
2.9780	6980	0.3537	-
2.9802	6985	0.3708	-
2.9823	6990	0.3649	-
2.9844	6995	0.3878	-
2.9866	7000	0.3452	-
2.9887	7005	0.3655	-
2.9908	7010	0.3878	-
2.9930	7015	0.3704	-
2.9951	7020	0.3715	-
2.9972	7025	0.3896	-
2.9994	7030	0.4088	-
3.0	7032	-	0.1704

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.1
PyLate: 1.3.4
Transformers: 4.56.2
PyTorch: 2.8.0+cu128
Accelerate: 1.12.0
Datasets: 4.4.1
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084"
}

PyLate

@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}

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Collection

Terminology Harmonization using Late-Interaction Reranker With Alignment-tuned Transformers • 3 items • Updated 12 days ago

Paper for sidataplus/THIRAWAT-BioLORD

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 10